Research work has been conducted in the field of data preparation, cleaning and augmentation using e. g. algorithms for interpolation, smoothing, simple transformation or filtering [1]}. Although these methods help to create the precondition for successful model development, they quickly appear constrained in their variation space. This article takes a closer look to a branch of research exploring the representation of artificially generated data based on real world blueprints, an approach also denoted synthetic data generation.
In this article we consider semantic segmentation on carbon fiber textiles with unique surface structures and heterogeneous anomalies. Earlier publications have shown that image processing based on conventional filters such as edge, contour, threshold or Fourier transformation do not cover anomalies with the desired accuracy. In this respect, we offer a CNN-based method using two differnt models for adverarial learning to augment the data. Thus, our DA methods are based on triginometric functions, Wasserstein Generative Adversarial Networks (WGANs) and pix2pix image-to-image translation. As an overall goal, we pursue this approach to improve the reliability of defect detection and reduce the costs of model training by automating a considerable portion of the data preparation tasks.
